-
diff --git a/doc/pub/DecisionTrees/html/._DecisionTrees-bs001.html b/doc/pub/DecisionTrees/html/._DecisionTrees-bs001.html index a53bdadad..f76edf0eb 100644 --- a/doc/pub/DecisionTrees/html/._DecisionTrees-bs001.html +++ b/doc/pub/DecisionTrees/html/._DecisionTrees-bs001.html @@ -282,6 +282,14 @@ MathJax.Hub.Config({
+We start here with the most basic algorithm, the so-called decision +tree. With this basic algorithm we can in turn build more complex +networks, spanning from homogeneous and heterogenous forests (bagging, +random forests and more) to one of the most popular supervised +algorithms nowadays, the extreme gradient boosting, or just +XGBoost. But let us start with the simplest possible ingredient. +
Decision trees are supervised learning algorithms used for both, classification and regression tasks. diff --git a/doc/pub/DecisionTrees/html/DecisionTrees-bs.html b/doc/pub/DecisionTrees/html/DecisionTrees-bs.html index b0e52796d..5dbe0927e 100644 --- a/doc/pub/DecisionTrees/html/DecisionTrees-bs.html +++ b/doc/pub/DecisionTrees/html/DecisionTrees-bs.html @@ -299,7 +299,7 @@ MathJax.Hub.Config({
-
diff --git a/doc/pub/DecisionTrees/html/DecisionTrees-reveal.html b/doc/pub/DecisionTrees/html/DecisionTrees-reveal.html index 8e8a5e858..9ab557f8e 100644 --- a/doc/pub/DecisionTrees/html/DecisionTrees-reveal.html +++ b/doc/pub/DecisionTrees/html/DecisionTrees-reveal.html @@ -148,7 +148,7 @@ MathJax.Hub.Config({
-
@@ -161,6 +161,14 @@ MathJax.Hub.Config({
+We start here with the most basic algorithm, the so-called decision
+tree. With this basic algorithm we can in turn build more complex
+networks, spanning from homogeneous and heterogenous forests (bagging,
+random forests and more) to one of the most popular supervised
+algorithms nowadays, the extreme gradient boosting, or just
+XGBoost. But let us start with the simplest possible ingredient.
+
Decision trees are supervised learning algorithms used for both,
classification and regression tasks.
diff --git a/doc/pub/DecisionTrees/html/DecisionTrees-solarized.html b/doc/pub/DecisionTrees/html/DecisionTrees-solarized.html
index 473793572..4bc0e2534 100644
--- a/doc/pub/DecisionTrees/html/DecisionTrees-solarized.html
+++ b/doc/pub/DecisionTrees/html/DecisionTrees-solarized.html
@@ -224,13 +224,21 @@ MathJax.Hub.Config({
-
+We start here with the most basic algorithm, the so-called decision
+tree. With this basic algorithm we can in turn build more complex
+networks, spanning from homogeneous and heterogenous forests (bagging,
+random forests and more) to one of the most popular supervised
+algorithms nowadays, the extreme gradient boosting, or just
+XGBoost. But let us start with the simplest possible ingredient.
+
Decision trees are supervised learning algorithms used for both,
classification and regression tasks.
diff --git a/doc/pub/DecisionTrees/html/DecisionTrees.html b/doc/pub/DecisionTrees/html/DecisionTrees.html
index 75a2c342c..38742eeb9 100644
--- a/doc/pub/DecisionTrees/html/DecisionTrees.html
+++ b/doc/pub/DecisionTrees/html/DecisionTrees.html
@@ -229,13 +229,21 @@ MathJax.Hub.Config({
-
+We start here with the most basic algorithm, the so-called decision
+tree. With this basic algorithm we can in turn build more complex
+networks, spanning from homogeneous and heterogenous forests (bagging,
+random forests and more) to one of the most popular supervised
+algorithms nowadays, the extreme gradient boosting, or just
+XGBoost. But let us start with the simplest possible ingredient.
+
Decision trees are supervised learning algorithms used for both,
classification and regression tasks.
diff --git a/doc/pub/DecisionTrees/ipynb/DecisionTrees.ipynb b/doc/pub/DecisionTrees/ipynb/DecisionTrees.ipynb
index d2132da47..a5c9bc63c 100644
--- a/doc/pub/DecisionTrees/ipynb/DecisionTrees.ipynb
+++ b/doc/pub/DecisionTrees/ipynb/DecisionTrees.ipynb
@@ -10,7 +10,7 @@
" \n",
"**Morten Hjorth-Jensen**, Department of Physics, University of Oslo and Department of Physics and Astronomy and National Superconducting Cyclotron Laboratory, Michigan State University\n",
"\n",
- "Date: **Dec 12, 2019**\n",
+ "Date: **Dec 26, 2019**\n",
"\n",
"Copyright 1999-2019, Morten Hjorth-Jensen. Released under CC Attribution-NonCommercial 4.0 license\n",
"\n",
@@ -20,6 +20,13 @@
"## Decision trees, overarching aims\n",
"\n",
"\n",
+ "We start here with the most basic algorithm, the so-called decision\n",
+ "tree. With this basic algorithm we can in turn build more complex\n",
+ "networks, spanning from homogeneous and heterogenous forests (bagging,\n",
+ "random forests and more) to one of the most popular supervised\n",
+ "algorithms nowadays, the extreme gradient boosting, or just\n",
+ "XGBoost. But let us start with the simplest possible ingredient.\n",
+ "\n",
"Decision trees are supervised learning algorithms used for both,\n",
"classification and regression tasks.\n",
"\n",
@@ -35,6 +42,8 @@
"informative** feature is done until we accomplish a stopping criteria\n",
"where we then finally end up in so called **leaf nodes**. \n",
"\n",
+ "\n",
+ "\n",
"A decision tree is typically divided into a **root node**, the **interior nodes**,\n",
"and the final **leaf nodes** or just **leaves**. These entities are then connected by so-called **branches**.\n",
"\n",
diff --git a/doc/pub/DecisionTrees/ipynb/ipynb-DecisionTrees-src.tar.gz b/doc/pub/DecisionTrees/ipynb/ipynb-DecisionTrees-src.tar.gz
index 3f00ab617..bcfd4eaf8 100644
Binary files a/doc/pub/DecisionTrees/ipynb/ipynb-DecisionTrees-src.tar.gz and b/doc/pub/DecisionTrees/ipynb/ipynb-DecisionTrees-src.tar.gz differ
diff --git a/doc/pub/DecisionTrees/pdf/DecisionTrees-minted.pdf b/doc/pub/DecisionTrees/pdf/DecisionTrees-minted.pdf
index fae007777..6f40060f3 100644
Binary files a/doc/pub/DecisionTrees/pdf/DecisionTrees-minted.pdf and b/doc/pub/DecisionTrees/pdf/DecisionTrees-minted.pdf differ
diff --git a/doc/src/DecisionTrees/DecisionTrees.do.txt b/doc/src/DecisionTrees/DecisionTrees.do.txt
index af97ea702..39c02a529 100644
--- a/doc/src/DecisionTrees/DecisionTrees.do.txt
+++ b/doc/src/DecisionTrees/DecisionTrees.do.txt
@@ -7,6 +7,13 @@ DATE: today
===== Decision trees, overarching aims =====
+We start here with the most basic algorithm, the so-called decision
+tree. With this basic algorithm we can in turn build more complex
+networks, spanning from homogeneous and heterogenous forests (bagging,
+random forests and more) to one of the most popular supervised
+algorithms nowadays, the extreme gradient boosting, or just
+XGBoost. But let us start with the simplest possible ingredient.
+
Decision trees are supervised learning algorithms used for both,
classification and regression tasks.
@@ -22,6 +29,8 @@ to be the most informative ones. The process of finding the _most
informative_ feature is done until we accomplish a stopping criteria
where we then finally end up in so called _leaf nodes_.
+
+
A decision tree is typically divided into a _root node_, the _interior nodes_,
and the final _leaf nodes_ or just _leaves_. These entities are then connected by so-called _branches_.
Decision trees, overarching aims
+
Dec 12, 2019
Dec 26, 2019
Decision trees, overarching aims
+
Dec 12, 2019
Dec 26, 2019
Decision trees, overarching aims
+